The largest wildfires in the United States occur in the western part of the country; an area with a population of over 77 million people. Smoke from wildfires is harmful to humans and reducing smoke-related illnesses requires limiting outdoor work and other activities in areas impacted by fire. These areas of impact can extend several hundreds of miles away from the fire, thus accurate prediction is critical to protect human health. Currently, weather and air quality models are used to provide smoke warnings to the public. However, there is a critical need to improve these models to correctly forecast the smoke transport over mountains. This research project will transform these models and advance the state-of-the-science of modeling the dynamic atmosphere over mountainous terrain. The new model will be used to develop an online smoke forecasting tool that provides warnings to protect human health during wildfire events in the western U.S. This project will enhance undergraduate STEM education by developing an online computational modeling course that fills a gap in existing curricula. Together, these educational products will enhance our ability to protect human health through accurate wildfire forecasting, while training the next generation of forecasters and increasing the scientific literacy of the Nation.
Regional scale air models that simulate wildfire smoke transport have significant uncertainties over mountainous terrain due to the complexities of the atmospheric flows and difficulties in estimating smoke plume injection heights. To improve smoke plume forecasts there is a critical need to develop new models that reduce uncertainties associated with both meteorological conditions and emissions modeling. The aim of this research is to use a cross-disciplinary approach to improve our fundamental understanding of wildfire smoke plume dynamics and complex atmospheric flows governing smoke transport over mountainous terrain. Successful completion of this work has the potential to transform our knowledge of the coupled atmosphere-fire-human system and greatly improve our ability to protect human health. The central hypothesis is that atmospheric turbulence parameterizations developed for flat terrain do not correctly simulate the planetary boundary layer structure over mountainous terrain leading to biases in the chemical transport modeling of wildfire smoke plumes. The four research objectives of this project are to: 1) improve models for vertical mixing over mountains to reduce the uncertainties in modeling smoke plume transport, 2) develop a novel model for smoke plume injection height to improve the vertical distribution of wildfire smoke emissions concentrations and subsequent regional transport, 3) incorporate model improvements for atmospheric mixing and wildfire emissions into a chemical transport model to improve smoke plume forecasts in the western U.S., and 4) create an online smoke forecasting tool using the improved regional scale smoke plume transport model. The education and outreach objectives of this project are to enhance numerical educational opportunities for undergraduate students by developing an online course focusing on numerical weather prediction modeling and high-performance computing and increase collaboration with local stakeholders to provide unique service-learning style research experiences for undergraduate students related to wildfire science in Nevada. A primary product of this research will be an online smoke forecasting tool designed in collaboration with a data visualization and communication expert. This online tool will be shared with local stakeholders to help inform the public about poor air quality events by issuing warnings that aid in reducing wildfire smoke exposure.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
